Toward fault tolerant modelling for SCADA based electricity distribution networks, machine learning approach.

Maintaining electrical energy is a crucial issue, especially in developing countries with very limited possibilities and recourses. However, the increasing reliance on electrical appliances generates many challenges for operators to fix any fault optimally within minimum time. Even with numerous researches conducted in this area, very few were interested in minimizing the fault duration, especially in the developing countries with very limited resources. Since decision-making requires enough information within minimum time, the integration of information technology with the existing electrical grids is the most appropriate. In this paper, we propose precise and accurate load redistribution estimation models. While several modeling techniques exist, the proposed modeling techniques in this work are based on machine learning models: multiple linear regression, nonlinear regression, and classifier neural network models. The novelty of this work is it introduces a fault-tolerant approach that relies on machine learning and supervisory control and data acquisition system (SCADA). The purpose of this approach is to help electricity distribution companies to maintain power for the customers and to shorten the fault duration from many hours to the minimum possible time. The work was performed based on real data of smart grids split into zones of about 20 transformers. The models' input data collected from the sensors allocated in the power grid, make the grid becomes able to redistribute the loads by sufficient strategies. To test and validate the models, two powerful modeling tools were used: MATLAB and Anaconda-Python. The results showed an accuracy of about 97% with a standard deviation of 2.3%. The load redistribution was also presented in details. With such eager results, they approve the validity of our model in minimizing the fault duration, by helping the system in taking ideal actions within the optimal time.